A Fast Iterative Adaptive Approach for Scanning Radar Angular Superresolution

Abstract

Angular superresolution technique is of great significance in enhancing the azimuth resolution when the real aperture is constrained. Recently, based on weighted least squares (WLS), the iterative adaptive approach (IAA) has been applied to scanning radar angular superresolution. The resulting estimates present noticeably superior performance compared with the existing approaches. However, the improved performance of IAA comes at the cost of high computational complexity. In this paper, a scheme of fast IAA (IAA-F) is proposed for mitigating the computational burden. First, based on the circulant structure of the steering matrix, the covariance matrix and WLS estimates are rewritten by fast convolution. Second, according to the similar block tridiagonal property between the covariance matrix and Schur complement of its submatrix, the fast matrix inversion works in an improved divide and conquer (D&C) approach, by recursively breaking down the problem of fast inversion into the same subproblem. Numerical results illustrate that the proposed IAA-F offers a time complexity reduction without loss of performance.